The quantitative analysis of in vivo medical images of the human brain is a growing field of activity and research. A general approach for diagnosis is to detect subtle differences in the composition, morphology or other behavior in the brain as can be imaged by different techniques and equipment (ie. modalities) and relate these differences to clinical phenomena of interest.
Image data can be obtained from various sources including T1 weighted Magnetic Resonance Imaging (“T1w MRI”), T2 weighted MRI (“T2w MRI”), Proton Density weighted MRI (“PD MRI”), Photon Emission Tomography (“PET”), Single Photon Emission Computer Tomography (“SPECT”) and Computer Tomography (“CT”).
Classification of neurological diseases based solely on their imaging characteristics is a challenging task for computer vision. If successful, however, classification systems based on image data of the brain, can serve multiple purposes such as computer-assisted diagnosis, disease characterization or the morphological assessment of drug effect.
Most of the work to date on automated or semi-automated classification of various neurological diseases performed using MRI images of the human brain, such as T1w MRI, has focused on individual brain structures that have either clear boundaries, or form a cohesive entity that can be segmented easily. Examples of the former include the ventricles and corpus callosum, while one of the most notable cases of the latter is the hippocampus (HC), a medial temporal lobe (MTL) structure that plays a central role in many pathological processes.
Volumetry in the context of the study of the brain, relates generally to taking various measurements of the volume of a structure within of the brain, and reaching conclusions based on such measurements. Based on manual or automated segmentation, it is the primary indicator of structure integrity. Volumetry results in epilepsy have been published in Jack C R, Jr., “MRI-based hippocampal volume measurements in epilepsy”, Epilepsia 1994, 35 Suppl 6: S21-9; Watson C, Cendes F, Fuerst D, Dubeau F, Williamson B, Evans A, Andermann F, “Specificity of volumetric magnetic resonance imaging in detecting hippocampal sclerosis”, Arch Neurol 1997, 54(1):67-73; and Bernasconi N, Bernasconi A, Caramanos Z, Antel S B, Andermann F, Arnold D L, “Mesial temporal damage in temporal lobe epilepsy: a volumetric MRI study of the hippocampus, amygdala and parahippocampal region”, Brain 2003; 126(Pt 2):462-9, the contents of each being incorporated herein by reference. Chetelat G, Baron J C, “Early diagnosis of Alzheimer's disease: contribution of structural neuroimaging”, Neuroimage 2003, 18(2):525-41, proposes a review of the subject as relating to Alzheimer's dementia, the contents of which are incorporated herein by reference. Obtaining manual volumetric results is resource intensive and necessitates neuroanatomical expertise.
In looking at a volume of the brain, the T1w MRI intensity can be used as an indicator of the progression of a disease, where subtle changes in the signal may indicate an underlying pathological process before structure integrity is lost. Some methods have used the intensity signal directly, such as Webb et al. in an application on temporal lobe epilepsy described in Webb J, Guimond A, Eldridge P, Chadwick D, Meunier J, Thirion J P, Roberts N, “Automatic detection of hippocampal atrophy on magnetic resonance images”, Magn Reson Imaging 1999, 17(8): 1149-61, the contents of which are incorporated herein by reference. Others have employed higher order statistics for texture (voxel by voxel) analysis to identify cortical abnormalities in epilepsy and lateralize the seizure focus, as in Antel S B, Collins D L, Bernasconi N, Andermann F, Shinghal R, Kearney R E, Arnold D L, Bernasconi A, “Automated detection of focal cortical dysplasia lesions using computational models of their MRI characteristics and texture analysis”, Neuroimage 2003, 19(4):1748-59, the contents of which are incorporated herein by reference.
Registration is a process also used in studying images of the brain. Individual subject images are aligned into a reference space, allowing spatial comparisons to be made between cohorts at the voxel level, such as in voxel-based morphometry or VBM as in Ashburner J, Friston K J, “Voxel-based morphometry—the methods”, Neuroimage 2000, 11(6 Pt 1):805-21, the contents of which are incorporated herein by reference. Examples of VBM analysis in epilepsy research include Woermann F G, Free S L, Koepp M J, Ashburner J, Duncan J S, “Voxel-by-voxel comparison of automatically segmented cerebral gray matter—A rater-independent comparison of structural MRI in patients with epilepsy”, Neuroimage 1999, 10(4):373-84; Keller S S, Wieshmann U C, Mackay C E, Denby C E, Webb J, Roberts N, “Voxel based morphometry of grey matter abnormalities in patients with medically intractable temporal lobe epilepsy: effects of side of seizure onset and epilepsy duration”, J Neurol Neurosurg Psychiatry 2002, 73(6):648-55 and Bernasconi N, Duchesne S, Janke A, Lerch J, Collins D L, Bernasconi A, “Whole-brain voxel-based statistical analysis of gray matter and white matter in temporal lobe epilepsy”, Neuroimage 2004, 23(2):717-23, the contents of each being incorporated herein by reference.
The registration process is typically broken down in a two-phase process to identify the linear and non-linear components required to align datasets. Linear transformation is used to correct global differences in brain size, orientation and shape. In a non-linear registration phase, a dense deformation field is estimated, which embeds unique spatial information about the individual brain under study. Morphometry based on the analysis of the deformation field is then possible, as proposed by Shen D, Moffat S, Resnick S M, Davatzikos C, “Measuring size and shape of the HC in MR images using a deformable shape model”, Neuroimage 2002, 15(2):422-34 or Chung M K, Worsley K J, Robbins S, Paus T, Taylor J, Giedd J N, Rapoport J L, Evans A C, “Deformation-based surface morphometry applied to gray matter deformation”, Neuroimage 2003, 18(2):198-213, the contents of each being incorporated herein by reference. This in turn enables surface analysis of individual structures to be conducted, such as analysis of the HC in Alzheimer's disease in Csernansky J G, Wang L, Joshi S, Miller J P, Gado M, Kido D, McKeel D, Morris J C, Miller M I, “Early Dementia of the Alzheimer type is distinguished from aging by high-dimensional mapping of the hippocampus”, Neurology 2000, 55(11):1636-43 or schizophrenia in Csernansky J G, Schindler M K, Splinter N R, Wang L, Gado M, Selemon L D, Rastogi-Cruz D, Posener J A, Thompson P A, Miller M I, “Abnormalities of thalamic volume and shape in schizophrenia”, Am J Psychiatry 2004, 161(5):896-902, the contents of each being incorporated herein by reference. Segmentation can be automated using a registration-based approach; once the structure has been identified, one can perform volumetric measurements as in Hogan R E, Bucholz R D, Choudhuri I, Mark K E, Butler C S, Joshi S, “Shape analysis of hippocampal surface structure in patients with unilateral mesial temporal sclerosis. J Digit Imaging 2000”, 13(2 Suppl 1):39-42, or further analysis of intrinsic properties, such as medial sheets (which can be crudely thought of as the planar skeleton of an object) as described in Styner M, Gerig G, Lieberman J, Jones D, Weinberger D, “Statistical shape analysis of neuroanatomical structures based on medial models”, Med Image Anal 2003, 7(3):207-20 and Joshi S, Pizer S, Fletcher P T, Yushkevich P, Thall A, Marron J S, “Multiscale deformable model segmentation and statistical shape analysis using medial descriptions”, IEEE Trans Med Imaging 2002, 21(5):538-50, the contents of each being incorporated herein by reference.
The drawbacks of structure-centered analysis reside mostly in their reliance on manual or automated segmentation, a process with its own limitations. Moreover, interrelations between neighboring structures, critical in many pathologies, are not captured if only individual elements are measured.
It should also be noted that the analytical techniques referenced above use either intensity or registration information, one at the exclusion of the other.